WO2019153443A1 - Procédé de correction auto-adaptative d'imagerie pondérée par diffusion par résonance magnétique - Google Patents

Procédé de correction auto-adaptative d'imagerie pondérée par diffusion par résonance magnétique Download PDF

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Publication number
WO2019153443A1
WO2019153443A1 PCT/CN2018/080113 CN2018080113W WO2019153443A1 WO 2019153443 A1 WO2019153443 A1 WO 2019153443A1 CN 2018080113 W CN2018080113 W CN 2018080113W WO 2019153443 A1 WO2019153443 A1 WO 2019153443A1
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diffusion weighted
magnetic resonance
correction method
adaptive correction
vector
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PCT/CN2018/080113
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English (en)
Chinese (zh)
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罗海
王世杰
朱高杰
周翔
陈梅泞
王超
刘霞
吴子岳
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奥泰医疗系统有限责任公司
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Publication of WO2019153443A1 publication Critical patent/WO2019153443A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/003—Reconstruction from projections, e.g. tomography
    • G06T11/005—Specific pre-processing for tomographic reconstruction, e.g. calibration, source positioning, rebinning, scatter correction, retrospective gating
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]

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  • the present invention relates to the field of magnetic resonance imaging, and more particularly to an adaptive correction method for magnetic resonance diffusion weighted imaging.
  • Diffusion Weighted Imaging is an imaging method that non-invasively reflects the irregular thermal motion of living water molecules at the molecular level. Imaging depends mainly on the motion of water molecules rather than the proton density of tissue, T1 or T2 relaxation time. Diffusion-weighted imaging is suitable for detecting the micro-dynamic and micro-structural changes of biological tissues at the level of living cells, and plays an important role in the benign and malignant identification, therapeutic evaluation and prediction of tumors.
  • the applied diffusion gradient is extremely sensitive to motion.
  • Exercise mainly includes the following four aspects: (1) diffuse movement of water molecules; (2) unconscious physiological movements of patients, such as respiratory movements, gastrointestinal motility, blood flow, etc.; (3) conscious or unconscious overall movement of patients; (4) dispersion System vibration caused by gradients.
  • the diffusion of water molecules under the influence of the diffusion gradient will cause a phase difference to reduce the tissue signal with a large diffusion coefficient, which is the principle of diffusion-weighted imaging.
  • the latter three movements will cause motion artifacts. Even sub-pixel motions will produce great phase differences, causing signal loss and serious artifacts.
  • the applied diffusion gradient is very large, which can cause the system to vibrate severely, which may cause radio frequency interference caused by loose coil interface or static electricity accumulation/release, and form strip-shaped artifacts in the image, usually called RF ignition. Artifacts.
  • the invention aims to provide an adaptive correction method for magnetic resonance diffusion weighted imaging. Based on the multiple acquisition averaging technique, the principal component analysis method is used to adaptively detect and correct motion artifacts and radio frequency ignition from redundant data. Shadows, etc., to better improve image quality without the need to add hardware devices.
  • An adaptive correction method for magnetic resonance diffusion weighted imaging comprising the following steps:
  • Step 1 repeatedly collecting the diffusion weighted image N times, N is a natural number, N ⁇ 3;
  • Step 2 construct a correlation matrix point by point based on the original image or the compressed image
  • Step 3 principal component analysis; obtaining a feature vector corresponding to a maximum eigenvalue of each correlation matrix;
  • Step 4 calculating a weight according to the feature vector
  • Step 5 Perform weighted synthesis on the original image collected in step 1 according to the weight obtained in step 4, to obtain a corrected diffusion weighted image.
  • step 2 all the collected original images are compressed by using an interpolation algorithm.
  • the benefits are that the first can reduce the amount of computation, and the second can increase the signal-to-noise ratio of the input data of the subsequent algorithm.
  • step 2 includes the following steps:
  • Step 2.1 for any pixel point (x, y) in the image acquired in the nth time, take the neighboring K points to form a neighborhood vector Xn;
  • Step 2.2 For N times of repeatedly acquired images, each pixel point corresponds to N neighborhood vectors, and the correlation between the nth vector Xn and the mth vector Xm is calculated according to formula (1);
  • x i is the i-th element in the vector Xn
  • y i is the i-th element in the vector Xm. Is the mean of the vector Xn, Is the mean of the vector Xm.
  • Step 2.3 any pixel point (x, y) corresponds to an N*N correlation matrix R(x, y);
  • r 1,1 ... r 1,N are the correlation coefficients between the two vectors calculated according to formula (1).
  • step 3 includes the following steps
  • Step 3.1 calculating the eigenvalues of the matrix R(x, y) to find the largest eigenvalue
  • step 3.2 the feature vector ⁇ corresponding to the maximum eigenvalue of the matrix R(x, y) is calculated.
  • the correlation matrix is first subjected to smoothing filtering processing.
  • the smoothing filtering process comprises the following steps;
  • Step a taking the i-th correlation coefficient from the correlation matrix R(x, y) corresponding to each pixel point (x, y), to form a matrix Ri of the same size as the image matrix;
  • Step b performing two-dimensional low-pass filtering on the matrix Ri;
  • Step c replacing the filtered result with the corresponding element in R(x, y);
  • step d repeat a-c until all elements in R(x, y) have been processed.
  • step 4 the weight is calculated by formula (2);
  • ⁇ n is the nth element of the feature vector ⁇
  • ⁇ min is the smallest element of the feature vector ⁇
  • ⁇ max is the largest element of the feature vector ⁇
  • a and p are parameter control factors.
  • a 0.2
  • step 5 the original image is weighted and synthesized by the formula (3);
  • Equation (3) M n is the n-shot acquisition diffusion weighted original image, w n is the weight.
  • step 1 the diffusion weighted image is repeatedly acquired N times with the same scanning parameter.
  • the invention adopts a principal component analysis method on the basis of multiple acquisition averaging techniques, adaptively detects and corrects data from redundant data, suppresses motion artifacts, RF ignition artifacts, etc., and improves image quality; Hardware device, and image quality is better than multiple acquisition direct averaging techniques.
  • Figure 1 is a flow chart of the present invention
  • Figure 2 is the same scan parameters, 4 abdominal diffusion weighted images obtained in 4 acquisitions;
  • FIG. 3 is a diffusion-weighted image obtained by directly averaging the data collected four times in FIG. 2;
  • Figure 4 is a diffusion weighted image of the four acquisition data of Figure 2 corrected according to the method of the present invention
  • Figure 5 is a direct average technique synthesis, a diffusion-weighted image of the abdomen containing radio frequency ignition artifacts
  • Fig. 6 is a graph showing the abdominal diffusion weighted image corrected by the method of the present invention corresponding to the data in Fig. 5.
  • Step 1 the same scanning parameters, repeated acquisition of the diffusion weighted image N times, N is a natural number, N ⁇ 3;
  • Step 2 construct a correlation matrix point by point based on the original image or the compressed image: specifically including the following steps;
  • Step 2.1 for any pixel point (x, y) in the image acquired in the nth time, take the neighboring K points to form a neighborhood vector Xn;
  • Step 2.2 For N times of repeatedly acquired images, each pixel point corresponds to N neighborhood vectors, and the correlation between the nth vector Xn and the mth vector Xm is calculated according to formula (1);
  • x i is the i-th element in the vector Xn
  • y i is the i-th element in the vector Xm. Is the mean of the vector Xn, Is the mean of the vector Xm.
  • Step 2.3 any pixel point (x, y) corresponds to an N*N correlation matrix R(x, y);
  • r 1,1 ... r 1,N are the correlation coefficients between the two vectors calculated according to formula (1).
  • Step 3 Principal component analysis: obtaining a feature vector corresponding to a maximum eigenvalue of each correlation matrix; specifically comprising the following steps;
  • Step 3.1 calculating the eigenvalues of the matrix R(x, y) to find the largest eigenvalue
  • step 3.2 the feature vector ⁇ corresponding to the maximum eigenvalue of the matrix R(x, y) is calculated.
  • Step 4 calculating a weight according to formula (2);
  • ⁇ n is the nth element of the feature vector ⁇
  • ⁇ min is the smallest element of the feature vector ⁇
  • ⁇ max is the largest element of the feature vector ⁇
  • a and p are parameter control factors.
  • Step 5 Perform weighted synthesis on the original image collected in step 1 according to the weight obtained in step 4, to obtain a corrected diffusion weighted image. Specifically, the original image is weighted and synthesized by the formula (3);
  • Equation (3) M n is the n-shot acquisition diffusion weighted original image, w n is the weight.
  • Embodiment 1 The difference between this embodiment and Embodiment 1 is that all the collected original images are compressed by the interpolation algorithm before step 2.
  • the benefits are that the first can reduce the amount of computation, and the second can increase the signal-to-noise ratio of the input data of the subsequent algorithm.
  • the correlation matrix is subjected to smoothing processing before step 3.
  • the smoothing filtering process comprises the following steps;
  • Step a taking the i-th correlation coefficient from the correlation matrix R(x, y) corresponding to each pixel point (x, y), to form a matrix Ri of the same size as the image matrix;
  • Step b performing two-dimensional low-pass filtering on the matrix Ri;
  • Step c replacing the filtered result with the corresponding element in R(x, y);
  • step d repeat a-c until all elements in R(x, y) have been processed.
  • the diffusion-weighted image synthesized by direct averaging has a limited degree of artifact suppression and poor picture quality; as shown in FIG. 4, the image corrected by the method of the present invention is more accurate. As shown in Figures 5 and 6, the RF ignition artifacts in the image corrected by the method of the present invention are significantly reduced.
  • the invention adaptively calculates the weights of each scan data based on the principal component analysis method, performs weighted synthesis according to the obtained weights, suppresses motion artifacts, RF ignition artifacts, and improves image quality without requiring Add hardware devices.

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  • Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Engineering & Computer Science (AREA)
  • Pathology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Radiology & Medical Imaging (AREA)
  • Theoretical Computer Science (AREA)
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  • General Physics & Mathematics (AREA)
  • Biomedical Technology (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Medical Informatics (AREA)
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Abstract

Procédé de correction auto-adaptative d'imagerie pondérée par diffusion par résonance magnétique, comprenant les étapes suivantes : étape 1, acquérir de manière répétée des images pondérées par diffusion N fois selon les mêmes paramètres de balayage, N étant supérieur ou égal à 3 ; étape 2, construire des matrices de corrélation point par point sur la base d'images originales ou d'images compressées ; étape 3, effectuer une analyse des composants principaux après mise en œuvre d'un traitement de filtrage lisse sur les matrices de corrélation, de façon à obtenir un vecteur caractéristique correspondant à la valeur caractéristique maximale de chaque matrice de corrélation ; étape 4, calculer une pondération en fonction du vecteur caractéristique ; et étape 5, mettre en œuvre une synthèse pondérée sur les images originales en fonction de la pondération, de façon à obtenir les images pondérées par diffusion corrigées. Selon le procédé, sur la base d'une technique de moyennage de multiples acquisitions, un procédé d'analyse des composants principaux est utilisé, des données sont détectées et corrigées de manière auto-adaptative à partir de données redondantes, un artefact de mouvement, un artefact remarquable de fréquence radio, etc. sont limités, et la qualité d'image est améliorée ; aucun dispositif matériel n'a besoin d'être ajouté, et la qualité d'image est meilleure que celle obtenue sur la base d'une technique de moyennage direct de multiples acquisitions.
PCT/CN2018/080113 2018-02-09 2018-03-23 Procédé de correction auto-adaptative d'imagerie pondérée par diffusion par résonance magnétique WO2019153443A1 (fr)

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